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Comprehensive & Cost Effective Laboratory Monitoring of HIV/AIDS: an African Role Model

Comprehensive & Cost Effective Laboratory Monitoring of HIV/AIDS: an African Role Model

Published on: October 31, 2010

Data Sampling and Supervised Learning for HIV Literature Screening.

Hayda Almeida, Marie-Jean Meurs, Leila Kosseim

IEEE Transactions on Nanobioscience

|January 24, 2017

Summary

This summary is machine-generated.

This study introduces a supervised learning method to speed up HIV literature screening for systematic reviews. The approach effectively flags relevant documents, saving researchers significant time and effort.

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Last Updated: Mar 8, 2026

Comprehensive & Cost Effective Laboratory Monitoring of HIV/AIDS: an African Role Model

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Published on: October 31, 2010

An Affordable HIV-1 Drug Resistance Monitoring Method for Resource Limited Settings

An Affordable HIV-1 Drug Resistance Monitoring Method for Resource Limited Settings

Published on: March 30, 2014

Genotypic Inference of HIV-1 Tropism Using Population-based Sequencing of V3

Genotypic Inference of HIV-1 Tropism Using Population-based Sequencing of V3

Published on: December 27, 2010

Area of Science:

Biomedical Informatics
Computational Biology
Public Health

Background:

Manual screening of biomedical literature for systematic reviews is time-consuming and prone to errors.
Efficient identification of relevant documents is crucial for comprehensive systematic reviews.

Purpose of the Study:

To develop and evaluate a supervised learning approach to automate the screening of HIV literature.
To improve the efficiency and accuracy of identifying relevant documents for HIV systematic reviews.

Main Methods:

Developed a supervised learning model using data sampling, feature combinations, and feature selection.
Generated and evaluated 105 classification models.
Identified optimal models using Logistic Model Trees classifier with Bag-Of-Words and MeSH terms.

Main Results:

The best performing models accurately labeled the majority of relevant HIV literature documents.
The system demonstrated effectiveness in supporting the screening process for systematic reviews.
Achieved high accuracy in flagging potentially relevant documents for HIV research.

Conclusions:

The supervised learning approach significantly enhances the efficiency of HIV literature screening.
This tool can assist researchers in reviewing a larger volume of literature in less time.
The developed system is a valuable aid for conducting systematic reviews in HIV research.